One of the most significant issues in Internet of Things (IoT) cloud computing is scheduling tasks. Recent developments in IoT-based technologies have led to a meteoric rise in the demand for cloud storage. In order to load the IoT services onto cloud resources efficiently even while satisfying the requirements of the applications, sophisticated planning methodologies are required. This is important because several processes must be well prepared on different virtual machines to maximize resource usage and minimize waiting times. Different IoT application tasks can be difficult to schedule in a cloud-based computing architecture due to the heterogeneous features of IoT. With the rise in IoT sensors and the need to access information quickly and reliably, fog cloud computing is proposed for the integration of fog and cloud networks to meet these demands. One of the most important necessities in a fog cloud setting is efficient task scheduling, as this can help to lessen the time it takes for data to be processed and improve QoS (quality of service). The overall processing time of IoT programs should be kept as short as possible by effectively planning and managing their workloads, taking into account limitations such as task scheduling. Finding the ideal approach is challenging, especially for big data systems, because task scheduling is a complex issue. This research provides a Deep Learning Algorithm for Big data Task Scheduling System (DLA-BDTSS) for the Internet of Things (IoT) and cloud computing applications. When it comes to reducing energy costs and end-to-end delay, an optimized scheduling model based on deep learning is used to analyze and process various tasks. The method employs a multi-objective strategy to shorten the makespan and maximize resource consumption. A regional exploration search technique improves the optimization algorithm’s capacity to exploit data and avoid becoming stuck in local optimization. DLA-BDTSS was compared to other well-known task allocation methods in accurate trace information and the CloudSim tools. The investigation showed that DLA-BDTSS performed better than other well-known algorithms. It converged faster than different approaches, making it beneficial for big data task scheduling scenarios, and it obtained an 8.43 percent improvement in the outcomes. DLA-BDTSS obtained an 8.43% improvement in the outcomes with an execution time of 34 s and fitness value evaluation of 76.8%.

The need for data is growing steadily due to big data technologies and the Internet’s quick expansion, and the volume of data being generated is creating a significant need for data analysis. The Internet of Things (IoT) model has appeared as a crucial element for edge platforms. An IoT system has serious performance issues due to the enormous volume of data that many connected devices produce. Potential methods to increase resource consumption and responsive services’ adaptability in an IoT system include edge-cloud computation and networking function virtualization (NFV) techniques. In the edge environment, there is a service combination of many IoT applications. The significant transmission latency impacts the functionality of the entire network in the IoT communication procedure because of the data communication among various service components. As a result, this research proposes a new optimization technique for IoT service element installation in edge-cloud-hybrid systems, namely the IoT-based Service Components Optimization Model (IoT-SCOM), with the decrease of transmission latency as the optimization aim. Additionally, this research creates the IoT-SCOM model and optimizes it to choose the best deployment option with the least assured delay. The experimental findings demonstrate that the IoT-SCOM approach has greater accuracy and effectiveness for the difficulty of data-intensive service element installation in the edge-cloud environment compared to the existing methods and the stochastic optimization technique.

Identification of disease from therapeutic statistical evidences area single confronted task which can make a point of importance in the field of medical science. But according to the literature survey, it has been seen that still there are some chances that this challenging task can be fulfilled. In this research a feature ranking algorithm Random Forest is used for ranked the features of the attributes & later on four machine learning algorithm has been used i.e. Random forest, decision Tree, support Vector Machine & XG Boost classification algorithm to classify similar disease datasets like Jaundice, Malaria, Covid, Common cold, Typhoid, Dengue & Pneumonia. Comparison between the classifier is done on the basis of with ranking with feature selection & ranking without feature selection with the help of parameters of confusion matrix, Matthews’s correlation coefficient (MCC), area under the curve (AUC), Receiver Operating Characteristics Curve (ROC) & computational time. The results of the simulations shows the effectiveness of Covid like disease prediction is done by the feature selection ranking &classification algorithm.

Numerous Internet of Things (IoT) systems produce massive volumes of information that must be handled and answered in a quite short period. The growing energy usage related to the migration of data into the cloud is one of the biggest problems. Edge computation helps users unload the workload again from cloud near the source of the information that must be handled to save time, increase security, and reduce the congestion of networks. Therefore, in this paper, Optimized Energy Efficient Strategy (OEES) has been proposed for extracting, distributing, evaluating the data on the edge devices. In the initial stage of OEES, before the transmission state, the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT system. The initial stage is followed by the reconstructing and the processing state. The processed data is transmitted to the nodes through controlled deep learning techniques. The entire stage of data collection, transmission and data reduction between edge devices uses less energy. The experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive accuracy. Energy consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES scheme. Predictive accuracy is 97.5 percent, data performance rate was 97.65 percent, and execution time is 14.49 ms.

Thermal rectification occurs when heat current through a material is favored in one direction but not in the opposite direction. These materials, often called thermal diodes, have the potential to perform logic calculations with phonons. Rectification obtained with existing material systems is either too minor or too difficult to implement practically. Hence, we present a scheme to enable higher rectification using a single-wall carbon nanotube (SWCNT) that is covalently functionalized near one end with polyacetylene (PA) chains. This composite structure allows rectification R up to 204%, which is higher than the values reported for SWCNTs. Here, [Formula: see text], where [Formula: see text] and [Formula: see text] are the heat currents for forward and reverse bias, respectively. The interatomic interactions in the SWCNT-PA nanocomposite are nonlinear, i.e., they are anharmonic, which is a requirement for thermal rectification. Through atomistic simulations, we identify two additional conditions to accomplish thermal rectification at the nanoscale, namely, (1) structural asymmetry, and (2) that the influence of this asymmetry on thermal transport is temperature dependent. The optimum temperature difference to achieve the highest thermal rectification with the structure is 40-80 K.